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Unbiased Elimination of Negative Weights in Monte Carlo Samples
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We propose a novel method for the elimination of negative Monte Carlo event weights. The method is process-agnostic, independent of any analysis, and preserves all physical observables. We demonstrate the overall performance and systematic improvement with increasing event sample size, based on predictions for the production of a W boson with two jets calculated at next-to-leading order perturbation theory.
Forward citations
Cited by 5 Pith papers
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Optimal-Transport-Based Cell Resampling for Negative and Pathological Event Weights
IRC-safe optimal-transport metrics (EMD, sEMD) enable lower-bias cell resampling of negative-weight NLO Monte Carlo events without intermediate jet clustering.
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Data-parallel leading-order event generation in MadGraph5_aMC@NLO
CUDACPP gives MadGraph data-parallel helicity amplitudes, delivering linear SIMD CPU speed-ups and up to order-of-magnitude GPU speed-ups for high-multiplicity QCD event generation.
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A Demonstration of ARCANE Reweighting: Reducing the Sign Problem in the MC@NLO Generation of $e^+ e^- \rightarrow q \bar{q} + 1\, jet$ Events
ARCANE reweighting cuts the post-unweighting negative-event fraction in e+e- -> q qbar + 1 jet MC@NLO generation from about 2.25% to below 10^-5 while preserving the visible event distributions.
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ARCANE Reweighting: A Monte Carlo Technique to Tackle the Negative Weights Problem in Collider Event Generation
ARCANE reweighting adds a carefully designed, zero-average correction to event weights so that positive and negative pathways to the same event cancel, preserving all physical distributions.
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A Cell Resampler study of Negative Weights in Multi-jet Merged Samples
Cell resampling with an adjusted metric reduces negative Monte Carlo event weights in NLO-matched, multi-jet merged pp to gamma gamma plus jets samples with small distortions to most distributions.
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